{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn import preprocessing\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train shape:  (4209, 378)\n"
     ]
    }
   ],
   "source": [
    "train = pd.read_csv('mer_train.csv')\n",
    "print('Train shape: ', train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Too many features (colums) with not enough rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>y</th>\n",
       "      <th>X0</th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "      <th>X3</th>\n",
       "      <th>X4</th>\n",
       "      <th>X5</th>\n",
       "      <th>X6</th>\n",
       "      <th>X8</th>\n",
       "      <th>...</th>\n",
       "      <th>X375</th>\n",
       "      <th>X376</th>\n",
       "      <th>X377</th>\n",
       "      <th>X378</th>\n",
       "      <th>X379</th>\n",
       "      <th>X380</th>\n",
       "      <th>X382</th>\n",
       "      <th>X383</th>\n",
       "      <th>X384</th>\n",
       "      <th>X385</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>130.81</td>\n",
       "      <td>k</td>\n",
       "      <td>v</td>\n",
       "      <td>at</td>\n",
       "      <td>a</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>88.53</td>\n",
       "      <td>k</td>\n",
       "      <td>t</td>\n",
       "      <td>av</td>\n",
       "      <td>e</td>\n",
       "      <td>d</td>\n",
       "      <td>y</td>\n",
       "      <td>l</td>\n",
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       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>76.26</td>\n",
       "      <td>az</td>\n",
       "      <td>w</td>\n",
       "      <td>n</td>\n",
       "      <td>c</td>\n",
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       "      <td>x</td>\n",
       "      <td>j</td>\n",
       "      <td>x</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>80.62</td>\n",
       "      <td>az</td>\n",
       "      <td>t</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>d</td>\n",
       "      <td>x</td>\n",
       "      <td>l</td>\n",
       "      <td>e</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13</td>\n",
       "      <td>78.02</td>\n",
       "      <td>az</td>\n",
       "      <td>v</td>\n",
       "      <td>n</td>\n",
       "      <td>f</td>\n",
       "      <td>d</td>\n",
       "      <td>h</td>\n",
       "      <td>d</td>\n",
       "      <td>n</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>18</td>\n",
       "      <td>92.93</td>\n",
       "      <td>t</td>\n",
       "      <td>b</td>\n",
       "      <td>e</td>\n",
       "      <td>c</td>\n",
       "      <td>d</td>\n",
       "      <td>g</td>\n",
       "      <td>h</td>\n",
       "      <td>s</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>24</td>\n",
       "      <td>128.76</td>\n",
       "      <td>al</td>\n",
       "      <td>r</td>\n",
       "      <td>e</td>\n",
       "      <td>f</td>\n",
       "      <td>d</td>\n",
       "      <td>f</td>\n",
       "      <td>h</td>\n",
       "      <td>s</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>25</td>\n",
       "      <td>91.91</td>\n",
       "      <td>o</td>\n",
       "      <td>l</td>\n",
       "      <td>as</td>\n",
       "      <td>f</td>\n",
       "      <td>d</td>\n",
       "      <td>f</td>\n",
       "      <td>j</td>\n",
       "      <td>a</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>27</td>\n",
       "      <td>108.67</td>\n",
       "      <td>w</td>\n",
       "      <td>s</td>\n",
       "      <td>as</td>\n",
       "      <td>e</td>\n",
       "      <td>d</td>\n",
       "      <td>f</td>\n",
       "      <td>i</td>\n",
       "      <td>h</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>30</td>\n",
       "      <td>126.99</td>\n",
       "      <td>j</td>\n",
       "      <td>b</td>\n",
       "      <td>aq</td>\n",
       "      <td>c</td>\n",
       "      <td>d</td>\n",
       "      <td>f</td>\n",
       "      <td>a</td>\n",
       "      <td>e</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 378 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID       y  X0 X1  X2 X3 X4 X5 X6 X8  ...   X375  X376  X377  X378  X379  \\\n",
       "0   0  130.81   k  v  at  a  d  u  j  o  ...      0     0     1     0     0   \n",
       "1   6   88.53   k  t  av  e  d  y  l  o  ...      1     0     0     0     0   \n",
       "2   7   76.26  az  w   n  c  d  x  j  x  ...      0     0     0     0     0   \n",
       "3   9   80.62  az  t   n  f  d  x  l  e  ...      0     0     0     0     0   \n",
       "4  13   78.02  az  v   n  f  d  h  d  n  ...      0     0     0     0     0   \n",
       "5  18   92.93   t  b   e  c  d  g  h  s  ...      0     0     1     0     0   \n",
       "6  24  128.76  al  r   e  f  d  f  h  s  ...      0     0     0     0     0   \n",
       "7  25   91.91   o  l  as  f  d  f  j  a  ...      0     0     0     0     0   \n",
       "8  27  108.67   w  s  as  e  d  f  i  h  ...      1     0     0     0     0   \n",
       "9  30  126.99   j  b  aq  c  d  f  a  e  ...      0     0     1     0     0   \n",
       "\n",
       "   X380  X382  X383  X384  X385  \n",
       "0     0     0     0     0     0  \n",
       "1     0     0     0     0     0  \n",
       "2     0     1     0     0     0  \n",
       "3     0     0     0     0     0  \n",
       "4     0     0     0     0     0  \n",
       "5     0     0     0     0     0  \n",
       "6     0     0     0     0     0  \n",
       "7     0     0     0     0     0  \n",
       "8     0     0     0     0     0  \n",
       "9     0     0     0     0     0  \n",
       "\n",
       "[10 rows x 378 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Target Feature\n",
    "\n",
    "\"y\" is the time (in seconds) that the car took to pass testing for each variable. Let's see its distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2002aff93c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 6))\n",
    "n, bins, patches = plt.hist(train['y'], 50, facecolor='blue', alpha=0.75)\n",
    "plt.xlabel('y value in seconds')\n",
    "plt.ylabel('count')\n",
    "plt.title('Histogram of y value')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    4209.000000\n",
       "mean      100.669318\n",
       "std        12.679381\n",
       "min        72.110000\n",
       "25%        90.820000\n",
       "50%        99.150000\n",
       "75%       109.010000\n",
       "max       265.320000\n",
       "Name: y, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['y'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2002aebb7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 6))\n",
    "plt.scatter(range(train.shape[0]), np.sort(train['y'].values))\n",
    "plt.xlabel('index')\n",
    "plt.ylabel('y')\n",
    "plt.title(\"Time Distribution\")\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is one outlier which was the max time at 265 seconds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2002b3d6278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(train['y'].values)\n",
    "plt.xlabel('Row number')\n",
    "plt.ylabel('y value')\n",
    "plt.title('Change in y value over the data set')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2002b55a828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(train['y'][:100].values)\n",
    "plt.xlabel('Row number')\n",
    "plt.ylabel('y value')\n",
    "plt.title('Change in y value over the data set for the first 100 samples')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Features Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of features except ID and target feature: 376\n",
      "Feature types :\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "int64     368\n",
       "object      8\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols = [c for c in train.columns if 'X' in c]\n",
    "print('Number of features except ID and target feature: {}'.format(len(cols)))\n",
    "print('Feature types :')\n",
    "train[cols].dtypes.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Out of all features, we are given 8 categorical features and 368 integer features. What about the cardinality of our features? The following ideas and scripts were from XXX."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Constant features: 12 Binary features: 356 Categorical features: 8\n",
      "\n",
      "Constant features:  ['X11', 'X93', 'X107', 'X233', 'X235', 'X268', 'X289', 'X290', 'X293', 'X297', 'X330', 'X347']\n",
      "\n",
      "Categorical features:  ['X0', 'X1', 'X2', 'X3', 'X4', 'X5', 'X6', 'X8']\n"
     ]
    }
   ],
   "source": [
    "counts = [[], [], []]\n",
    "for c in cols:\n",
    "    typ = train[c].dtypes\n",
    "    uniq = len(train[c].unique())\n",
    "    if uniq == 1:\n",
    "        counts[0].append(c)\n",
    "    elif uniq == 2 and typ == np.int64:\n",
    "        counts[1].append(c)\n",
    "    else:\n",
    "        counts[2].append(c)\n",
    "print('Constant features: {} Binary features: {} Categorical features: {}\\n'.format(*[len(c) for c in counts]))\n",
    "print('Constant features: ', counts[0])\n",
    "print()\n",
    "print('Categorical features: ', counts[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interestingly, we have 12 features which only have a single value (0) in them - these are pretty useless for supervised algorithms, and should probably be dropped (unless you want to use them for anomaly detection in case a different value appears in the test set). \n",
    "\n",
    "The rest of our dataset is made up of many binary features, and 8 categorical features. Let's explore categorical features first.\n",
    "\n",
    "### Categorical Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of levels in category 'X0': \b 47\n",
      "Number of levels in category 'X1': \b 27\n",
      "Number of levels in category 'X2': \b 44\n",
      "Number of levels in category 'X3': \b  7\n",
      "Number of levels in category 'X4': \b  4\n",
      "Number of levels in category 'X5': \b 29\n",
      "Number of levels in category 'X6': \b 12\n",
      "Number of levels in category 'X8': \b 25\n"
     ]
    }
   ],
   "source": [
    "for cat in ['X0', 'X1', 'X2', 'X3', 'X4', 'X5', 'X6', 'X8']:\n",
    "    print(\"Number of levels in category '{0}': \\b {1:2}\".format(cat, train[cat].nunique()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I am extremely interested in EDA, so, let's go ahead to explore every categorical feature."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature X0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2002b4c3470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sort_X0 = train.groupby('X0').size()\\\n",
    "                    .sort_values(ascending=False)\\\n",
    "                    .index\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.countplot(x='X0', data=train, order = sort_X0)\n",
    "plt.xlabel('X0')\n",
    "plt.ylabel('Occurances')\n",
    "plt.title('Feature X0')\n",
    "sns.despine();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### X0 vs. target feature y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2002b648908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sort_y = train.groupby('X0')['y']\\\n",
    "                    .median()\\\n",
    "                    .sort_values(ascending=False)\\\n",
    "                    .index\n",
    "plt.figure(figsize = (14, 6))\n",
    "sns.boxplot(y='y', x='X0', data=train, order=sort_y)\n",
    "ax = plt.gca()\n",
    "ax.set_xticklabels(ax.get_xticklabels())\n",
    "plt.title('X0 vs. y value')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature X1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2002b412c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sort_X1 = train.groupby('X1').size()\\\n",
    "                    .sort_values(ascending=False)\\\n",
    "                    .index\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.countplot(x='X1', data=train, order = sort_X1)\n",
    "plt.xlabel('X1')\n",
    "plt.ylabel('Occurances')\n",
    "plt.title('Feature X1')\n",
    "sns.despine();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### X1 vs. target feature y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2002bd442e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sort_y = train.groupby('X1')['y']\\\n",
    "                    .median()\\\n",
    "                    .sort_values(ascending=False)\\\n",
    "                    .index\n",
    "plt.figure(figsize = (10, 6))\n",
    "sns.boxplot(y='y', x='X1', data=train, order=sort_y)\n",
    "ax = plt.gca()\n",
    "ax.set_xticklabels(ax.get_xticklabels())\n",
    "plt.title('X1 vs. y value')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature X2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2002af9f4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sort_X2 = train.groupby('X2').size()\\\n",
    "                    .sort_values(ascending=False)\\\n",
    "                    .index\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.countplot(x='X2', data=train, order = sort_X2)\n",
    "plt.xlabel('X2')\n",
    "plt.ylabel('Occurances')\n",
    "plt.title('Feature X2')\n",
    "sns.despine();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### X2 vs. target feature y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2002b1e1898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sort_y = train.groupby('X2')['y']\\\n",
    "                    .median()\\\n",
    "                    .sort_values(ascending=False)\\\n",
    "                    .index\n",
    "plt.figure(figsize = (12, 6))\n",
    "sns.boxplot(y='y', x='X2', data=train, order=sort_y)\n",
    "ax = plt.gca()\n",
    "ax.set_xticklabels(ax.get_xticklabels())\n",
    "plt.title('X2 vs. y value')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature X3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2002b1e1a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sort_X3 = train.groupby('X3').size()\\\n",
    "                    .sort_values(ascending=False)\\\n",
    "                    .index\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.countplot(x='X3', data=train, order = sort_X3)\n",
    "plt.xlabel('X3')\n",
    "plt.ylabel('Occurances')\n",
    "plt.title('Feature X3')\n",
    "sns.despine();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### X3 vs. target feature y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2002bb055f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sort_y = train.groupby('X3')['y']\\\n",
    "                    .median()\\\n",
    "                    .sort_values(ascending=False)\\\n",
    "                    .index\n",
    "plt.figure(figsize = (10, 6))\n",
    "sns.boxplot(y='y', x='X3', data=train, order = sort_y)\n",
    "ax = plt.gca()\n",
    "ax.set_xticklabels(ax.get_xticklabels())\n",
    "plt.title('X3 vs. y value')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature X4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2002ceddda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sort_X4 = train.groupby('X4').size()\\\n",
    "                    .sort_values(ascending=False)\\\n",
    "                    .index\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.countplot(x='X4', data=train, order = sort_X4)\n",
    "plt.xlabel('X4')\n",
    "plt.ylabel('Occurances')\n",
    "plt.title('Feature X4')\n",
    "sns.despine();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### X4 vs. target feature y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2002cda96d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sort_y = train.groupby('X4')['y']\\\n",
    "                    .median()\\\n",
    "                    .sort_values(ascending=False)\\\n",
    "                    .index\n",
    "plt.figure(figsize = (10, 6))\n",
    "sns.boxplot(y='y', x='X4', data=train, order = sort_y)\n",
    "ax = plt.gca()\n",
    "ax.set_xticklabels(ax.get_xticklabels())\n",
    "plt.title('X4 vs. y value')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature X5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2002bb4f780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sort_X5 = train.groupby('X5').size()\\\n",
    "                    .sort_values(ascending=False)\\\n",
    "                    .index\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.countplot(x='X5', data=train, order = sort_X5)\n",
    "plt.xlabel('X5')\n",
    "plt.ylabel('Occurances')\n",
    "plt.title('Feature X5')\n",
    "sns.despine();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### X5 vs. target feature y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2002b5a2780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sort_y = train.groupby('X5')['y']\\\n",
    "                    .median()\\\n",
    "                    .sort_values(ascending=False)\\\n",
    "                    .index\n",
    "plt.figure(figsize = (12, 6))\n",
    "sns.boxplot(y='y', x='X5', data=train, order=sort_y)\n",
    "ax = plt.gca()\n",
    "ax.set_xticklabels(ax.get_xticklabels())\n",
    "plt.title('X5 vs. y value')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature X6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAt0AAAGDCAYAAAD3W6zoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAHNpJREFUeJzt3XvYrWVdJ/DvD7aIZxC3J446MjrqNSmiYnYwaFRQAycctVQiRtRIPJRJNV01TTU26qhMZaKYpOZh8AAVIoiaZaNx8JCCjiQmO0i3w8EDmoC/+WM9++oVNu61D/de77v4fK7rvdZ67nWv9XwfXq+9v/vxXs9T3R0AAGCcXRYdAAAAlp3SDQAAgyndAAAwmNINAACDKd0AADCY0g0AAIMp3QAAMJjSDbBKVNWXqurbVfXNFT/33s7PfExVbdhRGefc57uq6pSbjL23qv5gxfb6qvqzqrqmqq6uqrfuzIwAO9u6RQcA4Ps8qbs/sOgQm1TVuu6+YSvfdkKSz1bV27r7Q1X11CQPTfKMFXPeneT8JPsnuS7Jg3dIYIBVyplugDWgqg6pqr+dzgx/qqoes+K1Y6vqkqr6RlV9saqeM43fIcn7ktx75ZnzqnpTVf3Oivd/39nw6Yz7S6vq00m+VVXrpve9q6o2VtVlVXXiLWXt7n9O8ktJXl9V+yU5Oclzuvub0+c/Nsm+SV7S3dd29/Xd/Ykd+d8LYLVRugFWuaraO8lfJvmdJHdN8stJ3lVV66cpX03yxCR3TnJskldV1UHd/a0khye5orvvOP1cMedun57kCUn2SPK9JH+e5FNJ9k5yWJIXVtXjbunN3f2mJP+Q5KIkZ3f32StePiTJ55OcVlX/r6rOr6ofnzMXwJqkdAOsLu+dzmZfU1XvncaekeSs7j6ru7/X3ecmuSDJEUnS3X/Z3f/QM3+V5JwkP7qdOU7u7su7+9tJHp5kfXf/dnd/t7u/mOT1SZ62hc/46yR7JXnLTcb3SfLYJB9Kcs8kr0xyRlXdbTszA6xaSjfA6nJUd+8x/Rw1je2f5Ckryvg1SX4kyb2SpKoOr6qPVdVV02tHJNneAnv5iuf7Z7ZEZeX+fy3JPW7pzVV1YGZn5P8oySur6jYrXv52ki9196nT0pK3T/t79HZmBli1lG6A1e/yJG9eUcb36O47dPfLquq2Sd6V5BVJ7tHdeyQ5K0lN7+3NfN63ktx+xfY9NzNn5fsuT3LZTfZ/p+4+YnNhq6qSvCHJq5M8f9rfS1dM+fQt5AJYWko3wOr3liRPqqrHVdWuVbX79OXHfZLsluS2STYmuaGqDs9s6cYmX0myV1XdZcXYJ5McUVV3rap7JnnhFvb/d0m+Pn258nZThgdX1cNvYf7zMjvT/nvd/b0kxyX5lap6wPT6e5LsWVXHTJ91dGZrxT86738QgLVG6QZY5br78iRHZrakY2NmZ55fkmSX7v5GkhOTvDPJ1Ul+JsmZK977uSRvS/LFaWnIvZO8ObMvRX4ps/Xf79jC/m9M8qQkD0lyWZKvZXYm+y43nVtV+yb5vSTHdfd3p/dfnNm67ddXVXX3VUl+KrPlJ9cmOSnJkd39ta39bwOwVlS3/4cPAABGcqYbAAAGU7oBAGAwpRsAAAZTugEAYDClGwAABlu36AAjPP7xj++zzz570TEAAFh+teUpS3qm+2tfc6lXAABWj6Us3QAAsJoo3QAAMJjSDQAAgyndAAAwmNINAACDKd0AADCY0g0AAIMp3QAAMJjSDQAAgyndAAAwmNINAACDKd0AADCY0g0AAIOtW3SAnWnja9+y6Ahbbf3znrHoCAAAbKdbVeledl8++ehFR9gq+514+qIjAADsFJaXAADAYEo3AAAMpnQDAMBgSjcAAAymdAMAwGBKNwAADKZ0AwDAYMNKd1W9saq+WlWfWTF216o6t6q+MD3uOY1XVZ1cVZdW1aer6qAV7zlmmv+FqjpmVF4AABhl5JnuNyV5/E3GTkpyXncfmOS8aTtJDk9y4PRzfJLXJrOSnuQ3kzwyySOS/Oamog4AAGvFsNLd3R9JctVNho9Mctr0/LQkR60Y/9Oe+ViSParqXkkel+Tc7r6qu69Ocm5uXuQBAGBV29lruu/R3VcmyfR492l87ySXr5i3YRq7pXEAAFgzVssXKWszY/0Dxm/+AVXHV9UFVXXBxo0bd2g4AADYHju7dH9lWjaS6fGr0/iGJPuumLdPkit+wPjNdPcp3X1wdx+8fv36HR4cAAC21c4u3Wcm2XQFkmOSnLFi/FnTVUwOSXLttPzk/UkeW1V7Tl+gfOw0BgAAa8a6UR9cVW9L8pgkd6uqDZldheRlSd5ZVccl+XKSp0zTz0pyRJJLk1yX5Ngk6e6rquq/JTl/mvfb3X3TL2cCAMCqNqx0d/fTb+GlwzYzt5OccAuf88Ykb9yB0QAAYKdaLV+kBACApaV0AwDAYEo3AAAMpnQDAMBgSjcAAAymdAMAwGBKNwAADKZ0AwDAYEo3AAAMpnQDAMBgSjcAAAymdAMAwGBKNwAADKZ0AwDAYEo3AAAMpnQDAMBgSjcAAAymdAMAwGBKNwAADKZ0AwDAYEo3AAAMpnQDAMBgSjcAAAymdAMAwGBKNwAADKZ0AwDAYEo3AAAMpnQDAMBgSjcAAAymdAMAwGBKNwAADKZ0AwDAYEo3AAAMpnQDAMBgSjcAAAymdAMAwGBKNwAADKZ0AwDAYEo3AAAMpnQDAMBgSjcAAAymdAMAwGBKNwAADKZ0AwDAYEo3AAAMpnQDAMBgSjcAAAymdAMAwGBKNwAADKZ0AwDAYEo3AAAMtpDSXVUvqqrPVtVnquptVbV7Vd2nqj5eVV+oqndU1W7T3NtO25dOrx+wiMwAALCtdnrprqq9k5yY5ODufnCSXZM8LcnvJ3lVdx+Y5Ookx01vOS7J1d19vySvmuYBAMCasajlJeuS3K6q1iW5fZIrkxya5PTp9dOSHDU9P3LazvT6YVVVOzErAABsl51eurv7n5K8IsmXMyvb1ya5MMk13X3DNG1Dkr2n53snuXx67w3T/L1u+rlVdXxVXVBVF2zcuHHsQQAAwFZYxPKSPTM7e32fJPdOcockh29mam96yw947V8Huk/p7oO7++D169fvqLgAALDdFrG85CeTXNbdG7v7+iTvTvLDSfaYlpskyT5Jrpieb0iyb5JMr98lyVU7NzIAAGy7RZTuLyc5pKpuP63NPizJxUk+lOToac4xSc6Ynp85bWd6/YPdfbMz3QAAsFotYk33xzP7QuRFSf5+ynBKkpcmeXFVXZrZmu1Tp7ecmmSvafzFSU7a2ZkBAGB7rNvylB2vu38zyW/eZPiLSR6xmbnfSfKUnZELAABGcEdKAAAYTOkGAIDBlG4AABhM6QYAgMGUbgAAGEzpBgCAwZRuAAAYTOkGAIDBlG4AABhM6QYAgMGUbgAAGEzpBgCAwZRuAAAYTOkGAIDBlG4AABhM6QYAgMGUbgAAGEzpBgCAwZRuAAAYTOkGAIDBlG4AABhM6QYAgMGUbgAAGEzpBgCAwZRuAAAYTOkGAIDBlG4AABhM6QYAgMGUbgAAGEzpBgCAwZRuAAAYTOkGAIDBlG4AABhM6QYAgMGUbgAAGEzpBgCAwZRuAAAYTOkGAIDBlG4AABhM6QYAgMGUbgAAGEzpBgCAwZRuAAAYbKtLd1XtUlV3HhEGAACW0Vylu6r+rKruXFV3SHJxks9X1UvGRgMAgOUw75nuB3b315McleSsJPsleeawVAAAsETWzTnvNlV1m8xK9x909/VV1QNzwc28/9QjFh1hqzzuuLMWHQEAWCXmPdP9uiRfSnKHJB+pqv2TfH1UKAAAWCZznenu7pOTnLxi6B+r6ifGRAIAgOUy7xcp71FVp1bV+6btByY5ZmgyAABYEvMuL3lTkvcnufe0/X+TvHBbd1pVe1TV6VX1uaq6pKoeVVV3rapzq+oL0+Oe09yqqpOr6tKq+nRVHbSt+wUAgEWYt3TfrbvfmeR7SdLdNyS5cTv2+5okZ3f3A5L8UJJLkpyU5LzuPjDJedN2khye5MDp5/gkr92O/QIAwE43b+n+VlXtlaSTpKoOSXLttuxwurHOjyU5NUm6+7vdfU2SI5OcNk07LbMrpWQa/9Oe+ViSParqXtuybwAAWIR5Lxn44iRnJvk3VfXRJOuTHL2N+7xvko1J/qSqfijJhUlekOQe3X1lknT3lVV192n+3kkuX/H+DdPYldu4fwAA2KnmvXrJRVX140nun6SSfL67r9+OfR6U5Pnd/fGqek3+dSnJ5tTmIt1sUtXxmS0/yX777beN0QAAYMeb9+olJyS5Y3d/trs/k+SOVfUL27jPDUk2dPfHp+3TMyvhX9m0bGR6/OqK+fuueP8+Sa646Yd29yndfXB3H7x+/fptjAYAADvevGu6nz2tu06SdPfVSZ69LTvs7n9OcnlV3X8aOizJxZktX9l0GcJjkpwxPT8zybOmq5gckuTaTctQAABgLZh3TfcuVVXdvemLlLsm2W079vv8JG+tqt2SfDHJsZn9A+CdVXVcki8neco096wkRyS5NMl101wAAFgz5i3d78+sEP9xZuupn5vk7G3daXd/MsnBm3npsM3M7SQnbOu+AABg0eYt3S9N8pwkz8vsi43nJHnDqFAAALBM5r16yfcyuymNG9MAAMBWmqt0V9Wjk/xWkv2n91RmKz/uOy4aAAAsh3mXl5ya5EWZ3chme27/DgAAtzrzlu5ru/t9Q5MAAMCSmrd0f6iqXp7k3Un+ZdNgd180JBUAACyReUv3I6fHlZf56ySH7tg4AACwfOa9eslPjA4CAADLat4z3amqJyR5UJLdN41192+PCAUAAMtkl3kmTXeifGpmt2+vzG7Rvv/AXAAAsDTmKt1Jfri7n5Xk6u7+r0kelWTfcbEAAGB5zFu6vzM9XldV905yfZL7jIkEAADLZd413X9eVXskeXmSizK7csnrh6UCAIAlssXSXVW7JDmvu69J8q6q+osku3f3tcPTAQDAEtji8pLu/l6SV67Y/heFGwAA5jfvmu5zquqnq6qGpgEAgCU075ruFye5Q5Ibquo7mV02sLv7zsOSAQDAkpj3jpR3Gh0EAACW1Vylu6p+bHPj3f2RHRsHAACWz7zLS16y4vnuSR6R5MIkh+7wRAAAsGTmXV7ypJXbVbVvkv8xJBEAACyZea9eclMbkjx4RwYBAIBlNe+a7v+V2V0ok1lRf0iST40KBQAAy2TeNd0XrHh+Q5K3dfdHB+QBAIClM2/pPj3Jd7r7xiSpql2r6vbdfd24aAAAsBzmXdN9XpLbrdi+XZIP7Pg4AACwfOYt3bt39zc3bUzPbz8mEgAALJd5S/e3quqgTRtV9bAk3x4TCQAAlsu8a7pfmOR/V9UV0/a9kjx1TCS49Xndmx+36Ahb7TnPfP+iIwDAmjHvzXHOr6oHJLl/kkryue6+fmgyAABYEnMtL6mqE5Lcobs/091/n+SOVfULY6MBAMBymHd5ybO7+w83bXT31VX17CR/NCYWsEyOfc/jFx1hq/3Jk89edAQAlsi8X6Tcpapq00ZV7ZpktzGRAABgucx7pvucJO+sqj/O7Hbwz0viNBAAAMxh3tL9G0meneS5mX2R8pwkp44KBQAAy+QHlu6qWpfk95Icm+TyzAr3vkkuy2xpyo2jAwIAwFq3pTXdL09y1yT37e6DuvuhSe6T5C5JXjE6HAAALIMtle4nZnblkm9sGpiePy/JESODAQDAsthS6e7u7s0M3pjZFyoBAIAt2FLpvriqnnXTwap6RpLPjYkEAADLZUtXLzkhybur6ueTXJjZ2e2HJ7ldkicPzgYAAEvhB5bu7v6nJI+sqkOTPCizq5e8r7vP2xnhAABgGcx1ne7u/mCSDw7OAgAAS2ne28ADAADbSOkGAIDBlG4AABhM6QYAgMGUbgAAGEzpBgCAwZRuAAAYTOkGAIDBFla6q2rXqvpEVf3FtH2fqvp4VX2hqt5RVbtN47edti+dXj9gUZkBAGBbLPJM9wuSXLJi+/eTvKq7D0xydZLjpvHjklzd3fdL8qppHgAArBkLKd1VtU+SJyR5w7RdSQ5Ncvo05bQkR03Pj5y2M71+2DQfAADWhEWd6X51kl9J8r1pe68k13T3DdP2hiR7T8/3TnJ5kkyvXzvNBwCANWGnl+6qemKSr3b3hSuHNzO153ht5eceX1UXVNUFGzdu3AFJAQBgx1jEme5HJ/mpqvpSkrdntqzk1Un2qKp105x9klwxPd+QZN8kmV6/S5Krbvqh3X1Kdx/c3QevX79+7BEAAMBW2Omlu7t/tbv36e4DkjwtyQe7+2eTfCjJ0dO0Y5KcMT0/c9rO9PoHu/tmZ7oBAGC1Wk3X6X5pkhdX1aWZrdk+dRo/Ncle0/iLk5y0oHwAALBN1m15yjjd/eEkH56efzHJIzYz5ztJnrJTgwEAwA60ms50AwDAUlK6AQBgMKUbAAAGU7oBAGAwpRsAAAZTugEAYDClGwAABlO6AQBgMKUbAAAGU7oBAGAwpRsAAAZTugEAYDClGwAABlO6AQBgMKUbAAAGU7oBAGAwpRsAAAZTugEAYDClGwAABlO6AQBgMKUbAAAGU7oBAGAwpRsAAAZTugEAYDClGwAABlO6AQBgMKUbAAAGU7oBAGAwpRsAAAZTugEAYDClGwAABlO6AQBgMKUbAAAGU7oBAGAwpRsAAAZTugEAYDClGwAABlO6AQBgMKUbAAAGU7oBAGAwpRsAAAZTugEAYDClGwAABlO6AQBgMKUbAAAGU7oBAGAwpRsAAAZTugEAYDClGwAABlO6AQBgMKUbAAAG2+mlu6r2raoPVdUlVfXZqnrBNH7Xqjq3qr4wPe45jVdVnVxVl1bVp6vqoJ2dGQAAtse6BezzhiS/1N0XVdWdklxYVecm+bkk53X3y6rqpCQnJXlpksOTHDj9PDLJa6dHgFXhCe95+aIjbLW/fPJLFh0B4FZlp5/p7u4ru/ui6fk3klySZO8kRyY5bZp2WpKjpudHJvnTnvlYkj2q6l47OTYAAGyzha7prqoDkjw0yceT3KO7r0xmxTzJ3adpeye5fMXbNkxjN/2s46vqgqq6YOPGjSNjAwDAVllY6a6qOyZ5V5IXdvfXf9DUzYz1zQa6T+nug7v74PXr1++omAAAsN0WUrqr6jaZFe63dve7p+GvbFo2Mj1+dRrfkGTfFW/fJ8kVOysrAABsr53+RcqqqiSnJrmku//nipfOTHJMkpdNj2esGP/Fqnp7Zl+gvHbTMhQAxnvi6W9ddISt8hdH/+yiIwDczCKuXvLoJM9M8vdV9clp7NcyK9vvrKrjknw5yVOm185KckSSS5Ncl+TYnRsXAAC2z04v3d39N9n8Ou0kOWwz8zvJCUNDAQDAQO5ICQAAgyndAAAwmNINAACDKd0AADCY0g0AAIMp3QAAMJjSDQAAgyndAAAwmNINAACDKd0AADCY0g0AAIMp3QAAMJjSDQAAgyndAAAwmNINAACDKd0AADCY0g0AAIMp3QAAMJjSDQAAgyndAAAwmNINAACDKd0AADCY0g0AAIMp3QAAMJjSDQAAgyndAAAwmNINAACDKd0AADCY0g0AAIMp3QAAMJjSDQAAgyndAAAwmNINAACDKd0AADCY0g0AAIMp3QAAMJjSDQAAgyndAAAwmNINAACDKd0AADCY0g0AAIOtW3QAAFiko04/b9ERtsp7jz5s0RGAbaB0A8CSOvE9ly86wlY7+cn7LjoCDGF5CQAADKZ0AwDAYJaXAABr0vve8bVFR9hqhz/1bouOwII40w0AAIMp3QAAMJjlJQAAq9CXXv3Pi46w1Q544T0XHWHVcqYbAAAGWzNnuqvq8Ulek2TXJG/o7pctOBIAANvoK6/5P4uOsFXu8YJHbdf718SZ7qraNckfJjk8yQOTPL2qHrjYVAAAMJ81UbqTPCLJpd39xe7+bpK3JzlywZkAAGAua6V0751k5b1sN0xjAACw6lV3LzrDFlXVU5I8rrv/87T9zCSP6O7nr5hzfJLjp837J/n8Tox4tyRr7wr983N8a9syH98yH1vi+NY6x7d2LfOxJY5vR/tadz9+S5PWyhcpNyTZd8X2PkmuWDmhu09JcsrODLVJVV3Q3QcvYt87g+Nb25b5+Jb52BLHt9Y5vrVrmY8tcXyLslaWl5yf5MCquk9V7ZbkaUnOXHAmAACYy5o4093dN1TVLyZ5f2aXDHxjd392wbEAAGAua6J0J0l3n5XkrEXnuAULWdayEzm+tW2Zj2+Zjy1xfGud41u7lvnYEse3EGvii5QAALCWrZU13QAAsGYp3WxRVf3tojOMVlW/VVW/vOgcO9qy/+6q6puLzsDWq6oDquozi84xyrIf3zK7tfzuqurEqrqkqt666Cy3JmtmTTeL090/vOgMbBu/OwA24xeSHN7dly06yK2JM93bqap+o6o+V1XnVtXblvRs6VKeTayqX6+qz1fVBzK7odLSWdbf3a1BVb23qi6sqs9ON/9aNrtW1eun4zunqm636EAjVNV9q+oTVfXwRWfZUarqWVX16ar6VFW9edF5BlhXVadNx3h6Vd1+0YF2pKr64yT3TXJmVb1o0Xl2pKp6RlX9XVV9sqpeV1W7LjrTSkr3dqiqg5P8dJKHJvmPSVbdhdjZvKp6WGbXe9/0u1uavxBZGj/f3Q/L7M+VE6tqr0UH2sEOTPKH3f2gJNdk9mfpUqmq+yd5V5Jju/v8RefZEarqQUl+Pcmh3f1DSV6w4Egj3D/JKd3975N8PbOzwkuju5+b2Q0Gf6K7X7XoPDtKVf27JE9N8ujufkiSG5P87GJTfT+le/v8SJIzuvvb3f2NJH++6EDM7UeTvKe7r+vur8fNllh9TqyqTyX5WGZ35D1wwXl2tMu6+5PT8wuTHLDALCOsT3JGkmesOM5lcGiS07v7a0nS3VctOM8Il3f3R6fnb8ns73pWv8OSPCzJ+VX1yWn7vouN9P2s6d4+tegAbBfXy2RVqqrHJPnJJI/q7uuq6sNJdl9oqB3vX1Y8vzHJsi0vuTbJ5UkenWSZbuZWWf4/O296fMt+vMuikpzW3b+66CC3xJnu7fM3SZ5UVbtX1R2TPGHRgZjbR5I8uapuV1V3SvKkRQeCFe6S5OqpcD8gySGLDsRW+26So5I8q6p+ZtFhdqDzkvynTcudququC84zwn5V9ajp+dMz+7ue1e+8JEdX1d2T2f82q2r/BWf6Ps50b4fuPr+qzkzyqST/mOSCzM5uLJul+1d+d19UVe9I8snMfnd/veBIsNLZSZ5bVZ9O8vnMlpiwxnT3t6rqiUnOrapvdfcZi860vbr7s1X1u0n+qqpuTPKJJD+32FQ73CVJjqmq1yX5QpLXLjgPc+jui6vqvyQ5p6p2SXJ9khMy+zt+VXBHyu1UVXfs7m9O327+SJLju/uiRefaUaazGRd196r61yIAwFriTPf2O6WqHpjZesvTlqxw3zvJh5O8YsFRAADWNGe6AQBgMF+kBACAwZRuAAAYTOkGAIDBlG6AW5mq2reqLtt0jeWq2nPa3r+q9quqc6rqkqq6uKoOWGxagOXgi5QAt0JV9StJ7tfdx0/XI/5Sd//36e6Xv9vd5043/fped1+30LAAS0DpBrgVqqrbJLkwyRuTPDvJQ5PcL8kp3f0ji8wGsIxcpxvgVqi7r6+ql2R298vHdvd3q+rfJrmmqt6d5D5JPpDkpO6+cZFZAZaBNd0At16HJ7kyyYOn7XVJfjTJLyd5eJL7Zvlu8Q2wEEo3wK1QVT0kyX9IckiSF1XVvZJsSPKJ7v5id9+Q5L1JDlpgTICloXQD3MpUVSV5bZIXdveXk7w8ySuSnJ9kz6paP009NMnFi0kJsFx8kRLgVqaqjk9yWHc/ddreNcnfJXlxkt2SvDJJZfZFy+O7+7uLygqwLJRuAAAYzPISAAAYTOkGAIDBlG4AABhM6QYAgMGUbgAAGEzpBgCAwZRuAAAYTOkGAIDB/j/mEJfu7J5UswAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2002bd759b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sort_X6 = train.groupby('X6').size()\\\n",
    "                    .sort_values(ascending=False)\\\n",
    "                    .index\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.countplot(x='X6', data=train, order = sort_X6)\n",
    "plt.xlabel('X6')\n",
    "plt.ylabel('Occurances')\n",
    "plt.title('Feature X6')\n",
    "sns.despine();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### X6 vs. target feature y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2002bf11748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sort_y = train.groupby('X6')['y']\\\n",
    "                     .median()\\\n",
    "                     .sort_values(ascending=False)\\\n",
    "                     .index\n",
    "plt.figure(figsize = (12, 6))\n",
    "sns.boxplot(y='y', x='X6', data=train, order=sort_y)\n",
    "ax = plt.gca()\n",
    "ax.set_xticklabels(ax.get_xticklabels())\n",
    "plt.title('X6 vs. y value')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature X8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2002bf11cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sort_X8 = train.groupby('X8').size()\\\n",
    "                    .sort_values(ascending=False)\\\n",
    "                    .index\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.countplot(x='X8', data=train, order = sort_X8)\n",
    "plt.xlabel('X8')\n",
    "plt.ylabel('Occurances')\n",
    "plt.title('Feature X8')\n",
    "sns.despine();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### X8 vs. target feature y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2002bc8c278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sort_y = train.groupby('X8')['y']\\\n",
    "                    .median()\\\n",
    "                    .sort_values(ascending=False)\\\n",
    "                    .index\n",
    "plt.figure(figsize = (12, 6))\n",
    "sns.boxplot(y='y', x='X8', data=train, order=sort_y)\n",
    "ax = plt.gca()\n",
    "ax.set_xticklabels(ax.get_xticklabels())\n",
    "plt.title('X8 vs. y value')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some categorical features have effects on the \"y\" and the \"X0\" seems have the highest effect."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the categorical features, we encode the levels as digits using Scikit-learn's MultiLabelBinarizer and treat them as new features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MultiLabelBinarizer\n",
    "mlb = MultiLabelBinarizer()\n",
    "\n",
    "X0_trans = mlb.fit_transform([{str(val)} for val in train['X0'].values])\n",
    "X1_trans = mlb.fit_transform([{str(val)} for val in train['X1'].values])\n",
    "X2_trans = mlb.fit_transform([{str(val)} for val in train['X2'].values])\n",
    "X3_trans = mlb.fit_transform([{str(val)} for val in train['X3'].values])\n",
    "X4_trans = mlb.fit_transform([{str(val)} for val in train['X4'].values])\n",
    "X5_trans = mlb.fit_transform([{str(val)} for val in train['X5'].values])\n",
    "X6_trans = mlb.fit_transform([{str(val)} for val in train['X6'].values])\n",
    "X8_trans = mlb.fit_transform([{str(val)} for val in train['X8'].values])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 1, 0],\n",
       "       ..., \n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 1, 0, 0]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X8_trans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we drop the constant features and categorical features which have been MultiLabelBinarized earlier, as well as our target feature \"y\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_new = train.drop(['y','X11', 'X93', 'X107', 'X233', 'X235', 'X268', 'X289', 'X290', 'X293', 'X297', 'X330', 'X347', 'X0', 'X1', 'X2', 'X3', 'X4', 'X5', 'X6', 'X8'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then add the encoded features to form the final dataset to be used with TPOT."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_new = np.hstack((train_new.values, X0_trans, X1_trans, X2_trans, X3_trans, X4_trans, X5_trans, X6_trans, X8_trans))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4209, 552)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.isnan(train_new).any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "552"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_new[0].size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TPOT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_class = train['y'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(train_new, train_class,\n",
    "                                                    train_size=0.75, test_size=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f9973821ad3d4ef68a4b9e16c5290bfc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
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       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Optimization Progress', max=120), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 1 - Current best internal CV score: -75.24530750732399\n",
      "Generation 2 - Current best internal CV score: -74.90881962449828\n",
      "Generation 3 - Current best internal CV score: -74.90881962449828\n",
      "Generation 4 - Current best internal CV score: -74.90881962449828\n",
      "Generation 5 - Current best internal CV score: -74.90881962449828\n",
      "\n",
      "Best pipeline: RandomForestRegressor(KNeighborsRegressor(input_matrix, n_neighbors=47, p=1, weights=uniform), bootstrap=True, max_features=0.25, min_samples_leaf=16, min_samples_split=4, n_estimators=100)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "TPOTRegressor(config_dict=None, crossover_rate=0.1, cv=5,\n",
       "       disable_update_check=False, early_stop=None, generations=5,\n",
       "       max_eval_time_mins=5, max_time_mins=None, memory=None,\n",
       "       mutation_rate=0.9, n_jobs=1, offspring_size=None,\n",
       "       periodic_checkpoint_folder=None, population_size=20,\n",
       "       random_state=None, scoring=None, subsample=1.0, use_dask=False,\n",
       "       verbosity=2, warm_start=False)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tpot import TPOTRegressor\n",
    "\n",
    "tpot = TPOTRegressor(generations=5, population_size=20, verbosity=2)\n",
    "tpot.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TPOT cross-validation MSE\n",
      "-55.9489985239\n"
     ]
    }
   ],
   "source": [
    "print(\"TPOT cross-validation MSE\")\n",
    "print(tpot.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE:\n",
      "55.9489985239\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "print('MSE:')\n",
    "print(mean_squared_error(y_test, tpot.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE:\n",
      "7.47990631786\n"
     ]
    }
   ],
   "source": [
    "print('RMSE:')\n",
    "print(np.sqrt(mean_squared_error(y_test, tpot.predict(X_test))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tpot.export('tpot_Mercedes_testing_time_pipeline.py')"
   ]
  }
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